{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# A demo of the Spectral Biclustering algorithm\n\n\nThis example demonstrates how to generate a checkerboard dataset and\nbicluster it using the Spectral Biclustering algorithm.\n\nThe data is generated with the ``make_checkerboard`` function, then\nshuffled and passed to the Spectral Biclustering algorithm. The rows\nand columns of the shuffled matrix are rearranged to show the\nbiclusters found by the algorithm.\n\nThe outer product of the row and column label vectors shows a\nrepresentation of the checkerboard structure.\n\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "print(__doc__)\n\n# Author: Kemal Eren <kemal@kemaleren.com>\n# License: BSD 3 clause\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom sklearn.datasets import make_checkerboard\nfrom sklearn.datasets import samples_generator as sg\nfrom sklearn.cluster.bicluster import SpectralBiclustering\nfrom sklearn.metrics import consensus_score\n\nn_clusters = (4, 3)\ndata, rows, columns = make_checkerboard(\n    shape=(300, 300), n_clusters=n_clusters, noise=10,\n    shuffle=False, random_state=0)\n\nplt.matshow(data, cmap=plt.cm.Blues)\nplt.title(\"Original dataset\")\n\ndata, row_idx, col_idx = sg._shuffle(data, random_state=0)\nplt.matshow(data, cmap=plt.cm.Blues)\nplt.title(\"Shuffled dataset\")\n\nmodel = SpectralBiclustering(n_clusters=n_clusters, method='log',\n                             random_state=0)\nmodel.fit(data)\nscore = consensus_score(model.biclusters_,\n                        (rows[:, row_idx], columns[:, col_idx]))\n\nprint(\"consensus score: {:.1f}\".format(score))\n\nfit_data = data[np.argsort(model.row_labels_)]\nfit_data = fit_data[:, np.argsort(model.column_labels_)]\n\nplt.matshow(fit_data, cmap=plt.cm.Blues)\nplt.title(\"After biclustering; rearranged to show biclusters\")\n\nplt.matshow(np.outer(np.sort(model.row_labels_) + 1,\n                     np.sort(model.column_labels_) + 1),\n            cmap=plt.cm.Blues)\nplt.title(\"Checkerboard structure of rearranged data\")\n\nplt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}